A brain-based device is a device that has a sensing system for receiving information, effectors that enable the device to move about, and a simulated nervous system which controls movement of the effectors in response to input from the sensing system to guide the behavior of the brain-based device in a real-world environment. The sensing system may include video and audio sensors which receive image and audio information from the real-world environment in which the device moves. The simulated nervous system may be implemented as a computer-based system which receives and processes the image and auditory information input to the brain-based device and outputs commands to the effectors to control the behavior of the device in the environment.
The simulated nervous system, while implemented in a computer-based system, emulates the human brain rather than a programmed computer which typically follows a set of precise executable instructions or which performs computations. That is, the brain is not a computer and follows neurobiological rather than computational principles in its construction. The brain has special features or organization and functions that are not believed to be consistent with the idea that it follows such a set of precise instructions or that it computes in the manner of a programmed computer. A comparison of the signals that a brain receives with those of a computer shows a number of features that are special to the brain. For example, the real world is not presented to the brain like a data storage medium storing an unambiguous series of signals that are presented to a programmed computer. Nonetheless, the brain enables humans (and animals) to sense their environment, categorize patterns out of a multitude of variable signals, and initiate movement. The ability of the nervous system to carry out perceptual categorization of different signals for sight, sound, etc. and divide them into coherent classes without a prearranged code is special and unmatched by present day computers, whether based on artificial intelligence (AI) principles or neural network constructions.
The visual system of the brain contains a variety of cortical regions which are specialized to different visual features. For example, one region responds to the color of an object, another separate region responds to the object's shape, while yet another region responds to any motion of the object. The brain will enable a human to see and distinguish in a scene, for example, a red airplane from a gray cloud both moving across a background of blue sky. Yet, no single region of the brain has superordinate control over the separate regions responding to color, shape and movement that coordinate color, shape and movement so that we see and distinguish a single object (e.g. the airplane) and distinguish it from other objects in the scene (e.g. the cloud and the sky).
The fact that there is no such single superordinate control region in the brain poses what is known as the “binding problem.” How do these functionally separated regions of the brain coordinate their activities in order to associate features belonging to individual objects and distinguish among different objects? It is this ability of the brain to so associate and distinguish different objects that enables us to move about in our real-world environment. A mobile brain-based device having a simulated nervous system that can control the behavior of the device in the rich environment of the real world therefore would have many advantages and uses.
Mechanisms proposed for solving the “binding problem” generally fall into one of two classes: (i) binding through the influence of “higher” attentional mechanisms of the brain, and (ii) selective synchronization of the “firing” of dynamically formed groups of neurons in the brain. In (i), the belief is that the brain through its parietal or frontal regions, “binds” objects by means of an executive mechanism, for example, a spotlight of attention that would combine visual features appearing at a single location in space, e.g. the red airplane or gray cloud against the background of a blue sky. In (ii), the belief is that the brain “binds” objects in an automatic, dynamic, and pre-attentive process through groups of neurons that become linked by selective synchronization of the firing of the neurons. These synchronized neuronal groups form within the brain into global patterns of activity, or circuits, corresponding to perceptual categories. This enables us to see, for example, a red, flying airplane as a single object distinct from other objects such as a gray, moving cloud.
Computer-based computational models of visual binding, as well as physical, mobile brain-based devices having a simulated nervous system, are known, Yet, neither provides emergent circuits in the computer model or in the simulated nervous system of the physical brain-based device that contribute to providing a device with a rich and variable behavior in the real-world environment, especially in environments that require preferential behavior towards one object among many in a scene. For example, it would be desirable to have a mobile brain-based device move about in an environment and have preferential behavior toward one object among many in a scene so as to be able to obtain images of that object via an on-board camera and to select that object via on-board grippers.
One prior computational computer model simulated the nervous system by representing nine neural areas analogous to nine cortical areas of the visual system of the brain. It also simulated “reward” and motor systems of the nervous system. The model had “reentrant connections” or circuits between the nine different cortical areas, which are connections that allow the cortical areas to interact with each other. This computational model showed the capabilities of reentrant circuits to result in binding; the computer model, however, had several limitations. The stimuli into the modeled nervous system came from a limited predefined set of simulated object shapes and these were of uniform scale, contrary to what is found in a real-world environment. Furthermore, the resulting modeled behavior did not emerge in a rich and noisy environment experienced by behaving organisms in the real world. A more detailed description of this computational model is given in the paper entitled “Reentry and the Problem of Integrating Multiple Cortical Areas: Simulation of Dynamic Integration in the Visual System”, by Tononi and Edelman, Cerebral Cortex, July/August 1992.
A prior physical, mobile brain-based device having a simulated nervous system does explore its environment and through this experience learns to develop adaptive behaviors. Such a prior mobile brain-based device is guided by the simulated nervous system which is implemented on a computer system. The simulation of the nervous system was based on the anatomy and physiology of vertebrate nervous systems, but as with any simulated nervous system, with many fewer neurons and a simpler architecture than is found in the brain. For this physical, mobile brain-based device, the nervous system was made up of six major neural areas analogous to the cortical and subcortical brain regions. These six major areas included: an auditory system, a visual system, a taste system, a motor system capable of triggering behavior, a visual tracking system, and a value system. A detailed description of this mobile brain-based device is given in the paper entitled “Machine Psychology: Autonomous Behavior, Perceptual Categorization and Conditioning in a Brain-based Device” by Krichmar and Edelman, Cerebral Cortex, August 2002. While this brain-based device does operate in a real-world environment, it does not implement, among many other things, reentrant connections, thereby limiting its ability to engage in visually guided behavior and in object discrimination in a real-world environment.